Egomotion refers to the three-dimensional movement of a camera within an environment. Egomotion estimation refers to estimating the movement of the camera within an environment based on a series of images captured by the camera. Egomotion estimation is an important task in the field of computer vision and in particular to understanding and reconstructing three-dimensional scenes. Scene understanding and reconstruction are important to computer vision-based operation of mobile machines such as, for example, vehicles and self-guided robots.
In order to understand or reconstruct a scene, computer vision techniques may be employed to segment image frames of the scene. Image segmentation, however, may be a difficult task when the camera is moving. Image segmentation techniques must take into account the movement of the camera, which may be achieved by estimating egomotion as a preliminary step.
One known egomotion estimate approach recognizes, tracks, and matches feature points in a series of image frames. Feature tracking, however, may be computationally expensive to process.
Another known egomotion estimation approach relies on landmarks in the image frame such as, for example, lane markers or text on the road surface. In practice, however, prominent landmarks on the road surface may not always be available.
An additional technique for estimating egomotion uses stereo vision. This technique uses at least two cameras to obtain image frames of the environment. As a result, the stereo vision approach increases the hardware costs of egomotion estimation.
A need exists for an accurate, fast, and computationally inexpensive approach for estimating egomotion.